{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T15:37:06Z","timestamp":1768923426372,"version":"3.49.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s11263-020-01425-9","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T08:02:57Z","timestamp":1614672177000},"page":"1650-1674","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Context-Enhanced Representation Learning for Single Image Deraining"],"prefix":"10.1007","volume":"129","author":[{"given":"Guoqing","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5943-1989","authenticated-orcid":false,"given":"Changming","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arcot","family":"Sowmya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,2]]},"reference":[{"key":"1425_CR1","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al. (2016). TensorFlow: A system for large-scale machine learning. In USENIX Symposium on Operating Systems Design and Implementation."},{"key":"1425_CR2","unstructured":"Abel, G., Krahenbuhl, P., Joost, V., & Bengio, T. (2018). Image-to-image translation for cross-domain disentanglement. In Advances in Neural Information Processing Systems (NeurIPS)."},{"issue":"2","key":"1425_CR3","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1007\/s11263-008-0200-2","volume":"86","author":"PC Barnum","year":"2010","unstructured":"Barnum, P. C., Narasimhan, S., & Kanade, T. (2010). Analysis of rain and snow in frequency space. International Journal of Computer Vision, 86(2), 256\u2013275.","journal-title":"International Journal of Computer Vision"},{"issue":"8","key":"1425_CR4","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798\u20131828.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"1425_CR5","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1007\/s11263-011-0421-7","volume":"93","author":"J Bossu","year":"2011","unstructured":"Bossu, J., Hauti\u00e8re, N., & Tarel, J. P. (2011). Rain or snow detection in image sequences through use of a histogram of orientation of streaks. International Journal of Computer Vision, 93(3), 348\u2013367.","journal-title":"International Journal of Computer Vision"},{"key":"1425_CR6","doi-asserted-by":"crossref","unstructured":"Brewer, N., & Liu, N. (2008). Using the shape characteristics of rain to identify and remove rain from video. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition.","DOI":"10.1007\/978-3-540-89689-0_49"},{"key":"1425_CR7","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems (NeurIPS)."},{"issue":"6","key":"1425_CR8","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1109\/TPAMI.2016.2596743","volume":"39","author":"Y Chen","year":"2016","unstructured":"Chen, Y., & Pock, T. (2016). Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1256\u20131272.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1425_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Y. L., & Hsu, C. T. (2013). A generalized low-rank appearance model for spatio-temporally correlated rainstreaks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/ICCV.2013.247"},{"key":"1425_CR10","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J. W., Kim, S. & Choo, J. (2018). StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2018.00916"},{"key":"1425_CR11","doi-asserted-by":"crossref","unstructured":"Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space. In Proceedings of the IEEE Conference on Image Processing (ICIP).","DOI":"10.1109\/ICIP.2007.4378954"},{"key":"1425_CR12","unstructured":"Diederik, P. K., & Max, W. (2013). Auto-encoding variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"1425_CR13","doi-asserted-by":"crossref","unstructured":"Eigen, D., Krishnan, D., & Fergus, R. (2013). Restoring an image taken through a window covered with dirt or rain. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV.2013.84"},{"issue":"6","key":"1425_CR14","doi-asserted-by":"publisher","first-page":"2944","DOI":"10.1109\/TIP.2017.2691802","volume":"26","author":"X Fu","year":"2017","unstructured":"Fu, X., Huang, J., Ding, X., Liao, Y., & Paisley, J. (2017a). Clearing the skies: A deep network architecture for single-image rain removal. IEEE Transactions on Image Processing, 26(6), 2944\u20132956.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1425_CR15","doi-asserted-by":"crossref","unstructured":"Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., & Paisley, J. (2017b). Removing rain from single images via a deep detail network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2017.186"},{"key":"1425_CR16","doi-asserted-by":"crossref","unstructured":"Garg, K., & Nayar, S. K. (2004). Detection and removal of rain from videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2004.1315077"},{"issue":"1","key":"1425_CR17","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11263-006-0028-6","volume":"75","author":"K Garg","year":"2007","unstructured":"Garg, K., & Nayar, S. K. (2007). Vision and rain. International Journal of Computer Vision, 75(1), 3\u201327.","journal-title":"International Journal of Computer Vision"},{"key":"1425_CR18","unstructured":"Ge, Y., Li, Z., Zhao, H., Yin, G., Yi, S., & Wang, X. (2018). FD-GAN: Pose-guided feature distilling GAN for robust person re-identification. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"1425_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2016.90"},{"key":"1425_CR20","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"1","key":"1425_CR21","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/TMM.2013.2284759","volume":"16","author":"DA Huang","year":"2013","unstructured":"Huang, D. A., Kang, L. W., Wang, Y. C., & Lin, C. W. (2013). Self-learning based image decomposition with applications to single image denoising. IEEE Transactions on Multimedia, 16(1), 83\u201393.","journal-title":"IEEE Transactions on Multimedia"},{"key":"1425_CR22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Laurens, V. D., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2017.243"},{"key":"1425_CR23","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, M., Belongie, S., & Kautz, J. (2018). Multimodal unsupervised image-to-image translation. In Proceedings of the European Conference on Computer Vision (ECCV).","DOI":"10.1007\/978-3-030-01219-9_11"},{"issue":"4","key":"1425_CR24","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3072959.3073659","volume":"36","author":"S Iizuka","year":"2017","unstructured":"Iizuka, S., Serra, E. S., & Ishikawa, H. (2017). Globally and locally consistent image completion. ACM Transactions on Graphics, 36(4), 107\u2013123.","journal-title":"ACM Transactions on Graphics"},{"key":"1425_CR25","unstructured":"Isola, P., Zhu, J. Y., Zhou, T. H., & Efros, A. A. (2007). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"issue":"4","key":"1425_CR26","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.1109\/TIP.2011.2179057","volume":"21","author":"LW Kang","year":"2011","unstructured":"Kang, L. W., Lin, C. W., & Fu, Y. H. (2011). Automatic single-image-based rain streaks removal via image decomposition. IEEE Transactions on Image Processing, 21(4), 1742\u20131755.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1425_CR27","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"1425_CR28","doi-asserted-by":"crossref","unstructured":"Li, G., He, X., Zhang, W., Chang, H., Dong, L., & Lin, L. (2018a). Non-locally enhanced encoder\u2013decoder network for single image deraining. In Proceedings of the ACM International Conference on Multimedia (ACMMM).","DOI":"10.1145\/3240508.3240636"},{"key":"1425_CR29","doi-asserted-by":"crossref","unstructured":"Li, S., Araujo, I. B., Ren, W., Wang, Z., Tokuda, E. K., Junior, R. H., et al. (2019). Single image deraining: A comprehensive benchmark analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2019.00396"},{"key":"1425_CR30","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, J., Lin, Z., Liu, H., & Zha, H. (2018b). Recurrent squeeze-and-excitation context aggregation net for single image deraining. In Proceedings of the European conference on computer vision (ECCV).","DOI":"10.1007\/978-3-030-01234-2_16"},{"key":"1425_CR31","doi-asserted-by":"crossref","unstructured":"Li, Y., Tan, R. T., Guo, X., Lu, J., & Brown, M. S. (2016). Rain streak removal using layer priors. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2016.299"},{"key":"1425_CR32","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., RoyChowdhury, A., & Maji, S. (2015). Bilinear CNN models for fine-grained visual recognition. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV.2015.170"},{"key":"1425_CR33","unstructured":"Locatello, F., Bauer, S., Lucic, M., Gelly, S., Sch\u00f6lkopf, B., & Bachem, O. (2018). Challenging common assumptions in the unsupervised learning of disentangled representations. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"1425_CR34","doi-asserted-by":"crossref","unstructured":"Luo, Y., Xu, Y., & Ji, H. (2015). Removing rain from a single image via discriminative sparse coding. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV.2015.388"},{"key":"1425_CR35","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2016). Adversarial autoencoders. In Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"1425_CR36","unstructured":"Mao, X., Shen, C., & Yang, Y. (2016). Image restoration using convolutional auto-encoders with symmetric skip connections. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"1425_CR37","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R., Wang, Z., & Smolley, S. P. (2017). Least squares generative adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV.2017.304"},{"key":"1425_CR38","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV.2001.937655"},{"key":"1425_CR39","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016). Context encoders: Feature learning by inpainting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2016.278"},{"key":"1425_CR40","doi-asserted-by":"crossref","unstructured":"Qian, R., Tan, R. T., Yang, W., Su, J., & Liu, J. (2018). Attentive generative adversarial network for raindrop removal from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2018.00263"},{"key":"1425_CR41","doi-asserted-by":"crossref","unstructured":"Ren, D., Zuo, W., Hu, Q., Zhu, P., & Meng., D. (2019). Progressive image deraining networks: A better and simpler baseline. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2019.00406"},{"key":"1425_CR42","doi-asserted-by":"crossref","unstructured":"Ren, W., Ma, L., Zhang, J., Pan, J., Cao, X., Liu, W., et al. (2018). Gated fusion network for single image dehazing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2018.00343"},{"key":"1425_CR43","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"1425_CR44","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s11263-014-0759-8","volume":"112","author":"V Santhaseelan","year":"2015","unstructured":"Santhaseelan, V., & Asari, V. K. (2015). Utilizing local phase information to remove rain from video. International Journal of Computer Vision, 112(1), 71\u201389.","journal-title":"International Journal of Computer Vision"},{"key":"1425_CR45","doi-asserted-by":"crossref","unstructured":"Shih, Y. F., Yeh, Y. M., Lin, Y. Y., Weng, M. F., Lu, Y. C., & Chuang, Y. Y. (2017). Deep co-occurrence feature learning for visual object recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2017.772"},{"key":"1425_CR46","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. In Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"1425_CR47","unstructured":"Sun, S. H., Fan, S. P., & Wang, Y. C. (2016). Exploiting image structural similarity for single image rain removal. In Proceedings of the IEEE Conference on Image Processing (ICIP)."},{"key":"1425_CR48","doi-asserted-by":"crossref","unstructured":"Tran, L., Yin, X., & Liu, X. (2017). Disentangled representation learning GAN for pose-invariant face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2017.141"},{"key":"1425_CR49","unstructured":"Tschannen, M., Bachem, O., & Lucic, M. (2018). Recent advances in autoencoder-based representation learning. In Advances in Neural Information Processing Systems (NeurIPS), Bayesian Deep Learning Workshop."},{"key":"1425_CR50","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371\u20133408.","journal-title":"Journal of Machine Learning Research"},{"key":"1425_CR51","doi-asserted-by":"crossref","unstructured":"Wang, G., Sun, C., & Sowmya, A. (2019a). ERL-Net: Entangled representation learning for single image de-raining. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV.2019.00574"},{"key":"1425_CR52","doi-asserted-by":"crossref","unstructured":"Wang, T., Yang, X., Xu, K., Chen, S., Zhang, Q., & Lau, R. W. (2019b). Spatial attentive single-image deraining with a high quality real rain dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2019.01255"},{"key":"1425_CR53","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., & He, K. (2018). Non-local neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1425_CR54","doi-asserted-by":"crossref","unstructured":"Wei, W., Meng, D., Zhao, Q., Xu, Z., & Wu, Y. (2019). Semi-supervised transfer learning for image rain removal. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2019.00400"},{"issue":"6","key":"1425_CR55","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1109\/TPAMI.2019.2895793","volume":"42","author":"W Yang","year":"2019","unstructured":"Yang, W., Tan, R. T., Feng, J., Guo, Z., Yan, S., & Liu, J. (2019). Joint rain detection and removal from a single image with contextualized deep networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(6), 1377\u20131393.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1425_CR56","doi-asserted-by":"crossref","unstructured":"Yang, W., Tan, R. T., Wang, S., Fang, Y., & Liu, J. (2020). Single image deraining: From model-based to data-driven and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence.","DOI":"10.1109\/TPAMI.2020.2995190"},{"key":"1425_CR57","doi-asserted-by":"crossref","unstructured":"Yasarla, R., & Patel, V. M. (2019). Uncertainty guided multi-scale residual learning- using a cycle spinning CNN for single image deraining. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2019.00860"},{"key":"1425_CR58","doi-asserted-by":"publisher","first-page":"4544","DOI":"10.1109\/TIP.2020.2973802","volume":"29","author":"R Yasarla","year":"2020","unstructured":"Yasarla, R., & Patel, V. M. (2020). Confidence measure guided single image de-raining. IEEE Transactions on Image Processing, 29, 4544\u20134555.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"9","key":"1425_CR59","doi-asserted-by":"publisher","first-page":"1721","DOI":"10.1109\/TPAMI.2015.2491937","volume":"38","author":"S You","year":"2015","unstructured":"You, S., Tan, R. T., Kawakami, R., Mukaigawa, Y., & Ikeuchi, K. (2015). Adherent raindrop modeling, detectionand removal in video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1721\u20131733.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1425_CR60","unstructured":"Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. In Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"1425_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, H., & Patel, V. M. (2018). Density-aware single image deraining using a multi-stream dense network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2018.00079"},{"issue":"7","key":"1425_CR62","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017a). Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26(7), 3142\u20133155.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1425_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017b). Learning deep CNN denoiser prior for image restoration. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR.2017.300"},{"issue":"9","key":"1425_CR64","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., & Zhang, L. (2018). FFDNet: Toward a fast and flexible solution for CNN-based image denoising. IEEE Transactions on Image Processing, 27(9), 4608\u20134622.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"9","key":"1425_CR65","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1109\/TPAMI.2018.2858759","volume":"41","author":"B Zhou","year":"2019","unstructured":"Zhou, B., Bau, D., Oliva, A., & Torralba, A. (2019). Interpreting deep visual representations via network dissection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(9), 2131\u20132145.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01425-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-020-01425-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01425-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T18:14:25Z","timestamp":1620238465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-020-01425-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,2]]},"references-count":65,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["1425"],"URL":"https:\/\/doi.org\/10.1007\/s11263-020-01425-9","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,2]]},"assertion":[{"value":"10 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}